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Community detection helps us simplify the complex configuration of networks,but communities are reliable only if they are statistically significant. Todetect statistically significant communities, a common approach is to resamplethe original network and analyze the communities. But resampling assumesindependence between samples, while the components of a network are inherentlydependent. Therefore, we must understand how breaking dependencies betweenresampled components affects the results of the significance analysis. Here weuse scientific communication as a model system to analyze this effect. Ourdataset includes citations among articles published in journals in the years1984-2010. We compare parametric resampling of citations with non-parametricarticle resampling. While citation resampling breaks link dependencies, articleresampling maintains such dependencies. We find that citation resamplingunderestimates the variance of link weights. Moreover, this underestimationexplains most of the differences in the significance analysis of ranking andclustering. Therefore, when only link weights are available and articleresampling is not an option, we suggest a simple parametric resampling schemethat generates link-weight variances close to the link-weight variances ofarticle resampling. Nevertheless, when we highlight and summarize importantstructural changes in science, the more dependencies we can maintain in theresampling scheme, the earlier we can predict structural change.

Resampling effects on significance analysis of network clustering and ranking